Overview

In this lesson we hope to learn: - How to draw diagnostic plots in base graphics - Colors - `ggplot2’ - Basic geoms - Layering and faceting plots - Putting it together

Graphics Matter

• Graphics are a huge part of R and R encourages its users to use visualization to aid and present analysis
• Graphics are hard to get right, but tools can help make it easier
• Lots of great resources to help available for this online

Base graphics

• R has base graphics which are useful for quickly doing some plots

hist(df$readSS)

Base graphics are simple

• You can use them to understand your data and do quick diagnostics
• The commands are pretty easy to remember
• You can do complex things with them if you like, but the syntax can be confusing

Base graphics has some limitations

• What if we try to do a scatterplot of lots of data?

plot(df$readSS, df$mathSS)

Basic Plot

• Don’t use R base graphics
• ggplot2 is pretty much the new standard in R
• Create beautiful, clear plots with a nice consistent set of language conventions

library(ggplot2)
qplot(readSS, mathSS, data = df)

• Consider this basic scatterplot

Variations

qplot(readSS, mathSS, data = df) + geom_smooth()

qplot(readSS, mathSS, data = df, alpha = I(0.3))

qplot(readSS, mathSS, data = df) + xlab("Reading Score") + ylab("Math Score")

qplot(readSS, mathSS, data = df, color = race) + scale_color_brewer(type = "qual", 
    palette = 2)

Visualization Philosophy

• Visualization is about thinking about what objects in our dataset map with what visual cues for the viewer
• liberal use of names(object) helps
• What is the best way to show discrete variables, continuous variables, and connected variables
• We also need to be unafraid about trying transformations of variables, recoding variables, and reshaping variables to tell the story we are looking for

Our data for this exercise

• As we now know, student data is quite fascinating
• We have many discrete attribute variables
• Race, gender, economics, etc.
• We have some continuous variables
• Test scores, attendance
• We have some grouping variables
• Schools, districts, students, teachers
• Rich ground to make a number of visualizations that are useful

A few pro tips

• Don’t be afraid to manipulate the data
• Aggregate, combine, rescale variables to make the story easier to understand
• Don’t use too much color
• People print your work, color blindness is more prevalent than you think
• Color is fickle. Monitors and projectors reproduce it differently.
• Don’t overwhelm the user
• Make your visual depictions as easy as possible to immediately understand that data
• Don’t plot everything all at once

Draw a Colorwheel to show this problem

colwheel <- "https://dl.dropbox.com/u/1811289/colorwheel.R"
dropbox_source(colwheel)
col.wheel("magenta", nearby = 2)

##  [1] "plum"        "violet"      "darkmagenta" "magenta4"    "magenta3"   
##  [6] "magenta2"    "magenta"     "magenta1"    "orchid4"     "orchid"     

col.wheel("orange", nearby = 2)

##  [1] "salmon1"       "darksalmon"    "orangered4"    "orangered3"   
##  [5] "coral"         "orangered2"    "orangered"     "orangered1"   
##  [9] "lightsalmon2"  "lightsalmon"   "peru"          "tan3"         
## [13] "darkorange2"   "darkorange4"   "darkorange3"   "darkorange1"  
## [17] "linen"         "bisque3"       "bisque1"       "bisque2"      
## [21] "darkorange"    "antiquewhite3" "antiquewhite1" "papayawhip"   
## [25] "moccasin"      "orange2"       "orange"        "orange1"      
## [29] "orange4"       "wheat4"        "orange3"       "wheat"        
## [33] "oldlace"      

col.wheel("brown", nearby = 2)

##  [1] "snow1"       "snow2"       "rosybrown"   "rosybrown1"  "rosybrown2" 
##  [6] "rosybrown3"  "rosybrown4"  "lightcoral"  "indianred"   "indianred1" 
## [11] "indianred3"  "brown"       "brown4"      "brown1"      "brown3"     
## [16] "brown2"      "firebrick"   "firebrick1"  "chocolate"   "chocolate4" 
## [21] "saddlebrown" "seashell3"   "seashell2"   "seashell4"   "sandybrown" 
## [26] "peachpuff2"  "peachpuff3" 

Start with a great example

Scary R Code

library(grid)
p1 <- qplot(readSS, ..density.., data = df, fill = race, position = "fill", 
    geom = "density") + scale_fill_brewer(type = "qual", palette = 2)

p2 <- qplot(readSS, ..fill.., data = df, fill = race, position = "fill", geom = "density") + 
    scale_fill_brewer(type = "qual", palette = 2) + ylim(c(0, 1)) + theme_bw() + 
    opts(legend.position = "none", axis.text.x = theme_blank(), axis.text.y = theme_blank(), 
        axis.ticks = theme_blank(), panel.margin = unit(0, "lines")) + ylab("") + 
    xlab("")

vp <- viewport(x = unit(0.65, "npc"), y = unit(0.73, "npc"), width = unit(0.2, 
    "npc"), height = unit(0.2, "npc"))
print(p1)
print(p2, vp = vp)

Now, how?

• ggplot2 can be understood as combining a few concepts
• aesthetics, geoms, layers, and scales
• Making high quality graphics is just a matter of applying the right data to the right aesthetics in an appropriate geom, adding layer for detail, and ensuring the scale is appropriate
• Let’s talk about how to do that next

Aesthetics

• aesthetics are the axes, colors, glyphs, and sizes of the data mapped to visual cues
• ggplot2 has an extended syntax that makes this obvious

ggplot(df, aes(x = readSS, y = mathSS)) + geom_point()

• We’ve seen this guy before
• This makes ggplot2 like a sublanguage under R
• aes says we are specifying aesthetics, here we specified x and y to make a two dimensional graphic

Layers

• Exactly what they sound like, each plot is a simple series of layers
• Show examples

Geoms

- Geoms are the way data is represented, you can think of it like a chart type in another programming language

Scales

• Scales are the way the numeric/categorical data is mapped to a visual representation
• They transform the geoms and aesthetics
• Scales preserve the mapping, but allow us to explore reshaping the data

Exercises

1. 2.

References

Session Info

It is good to include the session info, e.g. this document is produced with knitr version 0.7. Here is my session info:

print(sessionInfo(), locale = FALSE)

## R version 2.15.1 (2012-06-22)
## Platform: i386-pc-mingw32/i386 (32-bit)
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] stringr_0.6.1  quantreg_4.81  SparseM_0.96   lmtest_0.9-30 
##  [5] zoo_1.7-7      gridExtra_0.9  ggplot2_0.9.1  hexbin_1.26.0 
##  [9] lattice_0.20-6 mgcv_1.7-19    Cairo_1.5-1    knitr_0.7     
## [13] plyr_1.7.1    
## 
## loaded via a namespace (and not attached):
##  [1] colorspace_1.1-1   dichromat_1.2-4    digest_0.5.2      
##  [4] evaluate_0.4.2     formatR_0.6        labeling_0.1      
##  [7] MASS_7.3-19        Matrix_1.0-6       memoise_0.1       
## [10] munsell_0.3        nlme_3.1-104       proto_0.3-9.2     
## [13] RColorBrewer_1.0-5 reshape2_1.2.1     scales_0.2.1      
## [16] tools_2.15.1      

Attribution and License

This work (R Tutorial for Education, by Jared E. Knowles), in service of the Wisconsin Department of Public Instruction, is free of known copyright restrictions.